scipy.spatial.distance.

hamming#

scipy.spatial.distance.hamming(u, v, w=None)[source]#

Compute the Hamming distance between two arrays.

The Hamming distance between 1-D arrays u and v, is simply the proportion of disagreeing components in u and v. If u and v are boolean vectors, the Hamming distance is

\[\frac{c_{01} + c_{10}}{n}\]

where \(c_{ij}\) is the number of occurrences of \(\mathtt{u[k]} = i\) and \(\mathtt{v[k]} = j\) for \(k < n\).

Parameters:

u(…, N) array_like: Input array.
v(…, N) array_like: Input array.
w(…, N) array_like, optional: The weights for each value in u and v. Default is None, which gives each value a weight of 1.0

Returns:

hammingdouble or array: The Hamming distance between arrays u and v.

Notes

Array API Standard Support

hamming has support for Python Array API Standard compatible backends in addition to NumPy. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	✅
PyTorch	✅	✅
JAX	✅	✅
Dask	✅	n/a

See Support for the array API standard for more information.

Examples

>>> from scipy.spatial import distance
>>> distance.hamming([1, 0, 0], [0, 1, 0])
0.66666666666666663
>>> distance.hamming([1, 0, 0], [1, 1, 0])
0.33333333333333331
>>> distance.hamming([1, 0, 0], [2, 0, 0])
0.33333333333333331
>>> distance.hamming([1, 0, 0], [3, 0, 0])
0.33333333333333331